We close this Introduction with some brief remarks about anthropic reasoning. From a Bayesian perspective, anthropic reasoning is intermediate between prior predictive probabilities \(P(I|{\cal H})\) , which are unconditioned, and posterior probabilities \(P({\cal H} | D)\) , which are conditioned on our data. Anthropic conditionalization is an in-between [1]}, [2]}, whereby one attempts to condition on the existence of observers. An immediate difficulty, of course, lies in defining “observers" in sufficient generality,We would be hard-pressed to offer such a definition within our observable universe, let alone across a multiverse with varying physical constants. so one is forced in practice to condition on some observational proxy for the existence of observers. For instance, one approach is to consider \(P(I|{\cal H}\cap {\cal A})\)  [2]}, which are conditioned on the set of observational proxy \({\cal A}\)  within the anthropic window. In this sense, within a hypothesis, only the set of universes within this anthropic window is considered. The problem is that the choice of proxy (fraction of baryons per galaxy [4]}, entropy production [5]}, or number of observations [6]}) is ambiguous and can lead to very different results [6]}. For this reason, we avoid any anthropic conditionalization in this work. Our focus is on extracting as much information from prior predictive probabilities.
